In this section, we introduce how the continuum library could be used to create scenarios compatible with the use cases and settings presented in the previous sections. In the existing continual learning, many research papers share their research code and their environment such as [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}. However, their code base is not necessary designed to be adapted easily to other settings and contains only the scenarios of their experiments. Continuum is therefore developed to gathered most of the existing scenarios into one single codebase that could be easily used, extended and adapted.
Hence, Continuum is an open-source framework to design and use continual learning scenarios
Code available at https://github.com/Continvvm/continuum..
It was created to provide pre-made scenarios to continual learning researchers. The goal is to gain time and avoid bugs in a continual learning project. It is also made to improve reproducibility of results by providing normalized scenario settings. We wish to stress that the philosophy of Continuum is to be easily integrated to any continual learning projects irrespective of the models used. In this section, we will present the architecture of continuum to create scenarios. Moreover, we introduce the metrics that are provided by the framework to evaluate algorithms.
